1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
15/1/2024 Comida 98241 Tami Supermercado
17/1/2024 VTR 21990 Andrés NA
22/1/2024 Netflix 8304 Tami NA
22/1/2024 Comida 44016 Tami Supermercado
23/1/2024 Comida 7500 Andrés NA
27/1/2024 Jardinero 40000 Tami NA
29/1/2024 Comida 65786 Tami Supermercado
30/1/2024 Electricidad 55759 Andrés NA
3/2/2024 donación 50000 Andrés NA
4/2/2024 Comida 46309 Andrés NA
5/2/2024 Comida 33079 Tami Supermercado
8/2/2024 Enceres 35440 Andrés casaideas
18/2/2024 VTR 21990 Andrés NA
22/2/2024 Netflix 8393 Tami NA
25/2/2024 Enceres 4973 Andrés colgador manguera
25/2/2024 Enceres 7980 Andrés adaptador vorriente y adaptadores manguera
27/2/2024 Enceres 49980 Andrés detergente
28/2/2024 Enceres 12000 Andrés 2 cajas orgamizadoras
28/2/2024 Electricidad 56337 Andrés PAC ENEL_______ 00000001686518 28/02
29/2/2024 Gas 70997 Andrés 68 997 + propina 2 lks
3/3/2024 Comida 53553 Andrés Supermercado
4/3/2024 Comida 6000 Andrés pasas
5/3/2024 Uber cumple papá 8582 Tami NA
6/3/2024 Agua 16549 Andrés NA
7/3/2024 Enceres 4645 Andrés descuentos desodorantes
10/3/2024 Comida 7470 Andrés NA
10/3/2024 Comida 90504 Tami Supermercado
10/3/2024 Diosi 21081 Andrés pipeta
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.2504e+08   2    7.8585  4e-04 ***
## lag_depvar    9.9919e+10   1 1903.4499 <2e-16 ***
## Residuals     3.5643e+10 679                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   926.6511 13531.03 0.0197608
## 2-0 29302.705 23589.6485 35015.76 0.0000000
## 2-1 22073.867 18719.3723 25428.36 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
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## 241  30660.29             2   37895.43
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## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
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## 256  78380.00             2   58350.57
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## 259  72207.14             2   70510.86
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## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
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## 288  65588.57             2   66633.29
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## 291  52891.00             2   74644.71
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## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
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## 305  52968.43             2   57248.43
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## 314  33604.71             2   34541.86
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## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
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## 323 103790.29             2  105647.43
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## 325  74746.14             2   76122.29
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## 333  32582.71             2   33337.29
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## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
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## 358  40957.29             2   38509.29
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## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
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## 422  33094.86             2   46352.43
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## 424  26212.43             2   32784.86
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## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
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## 431  39328.29             2   39456.29
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## 435  29513.29             2   27709.00
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## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
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## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
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## 443  38988.14             2   41408.14
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## 445  38114.00             2   43555.29
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## 463  50720.71             2   50490.00
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## 467  52457.00             2   55515.57
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## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
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## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
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## 500  38709.00             2   35962.71
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## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
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## 507  42263.00             2   46545.43
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## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
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## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
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## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
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## 521  63705.14             2   62171.71
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## 531  62329.29             2   47835.71
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## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 677  32881.71             2   29734.86
## 678  38298.57             2   32881.71
## 679  40886.14             2   38298.57
## 680  38601.86             2   40886.14
## 681  38628.86             2   38601.86
## 682  39142.57             2   38628.86
## 683  32666.14             2   39142.57
## 684  39911.57             2   32666.14
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   527 51536.97 15361.487
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1935.13082   4004.48197   -502.41104   2468.98631  -2899.11453    544.37710 
##            8            9           10           11           12           13 
##  -5618.16309  -1230.24630  -4012.99467   -509.44516  -5018.12902  -1742.73020 
##           14           15           16           17           18           19 
##  -1032.19735    256.22058  -3335.60239   -498.81841  -2233.61679   6490.12044 
##           20           21           22           23           24           25 
##  -1524.43866  -1218.93953   1456.22136  -1174.28058    235.65000   1706.97393 
##           26           27           28           29           30           31 
##  -7059.05716    888.71875   8162.66303    520.20783     89.07236  -2302.93464 
##           32           33           34           35           36           37 
##   1634.10463   4654.01358   1273.02489   2543.47155  -1692.24758   4741.92797 
##           38           39           40           41           42           43 
##   4382.79062  -2142.54293  -2897.76889  -1080.09215 -10730.81348   7141.74918 
##           44           45           46           47           48           49 
##   2535.79765   1386.23622   8142.79951    838.56962   6672.67550   6936.57848 
##           50           51           52           53           54           55 
##  -5589.02800  -4624.72835  -4980.21143  -7932.61895   6010.65199  -4090.20847 
##           56           57           58           59           60           61 
##  -4965.35810   3722.58585    828.57503    -70.26193    109.04022  -5022.72330 
##           62           63           64           65           66           67 
##  18031.16193   3823.56726  -3432.21424   6059.50675   7548.88754  14926.35827 
##           68           69           70           71           72           73 
##   2159.90488 -12779.24466  -1120.24595   4787.59381  -4705.53779  -4305.41087 
##           74           75           76           77           78           79 
## -10474.58273   2333.95407  -5478.87415    916.41297  -6978.63124    349.03553 
##           80           81           82           83           84           85 
##  -2518.67946  -2870.92334  -4125.21035   -763.18337   2110.34503   3618.76561 
##           86           87           88           89           90           91 
##    406.83484   -538.18481    143.26675   4258.81769  -1137.13978   1157.07731 
##           92           93           94           95           96           97 
##  -2041.55646  -1053.82505    154.62647    257.96557  -7494.07135   2274.28418 
##           98           99          100          101          102          103 
##  -8669.45292  -3123.97550  -4241.77770  -1971.58242  -1490.98059   2962.77478 
##          104          105          106          107          108          109 
##  -2485.44827   2435.25611  -1258.52406    866.76534   2511.35780  -3182.09523 
##          110          111          112          113          114          115 
##  -4792.64546   -979.32999   1779.06371  11613.31551  -1141.21135   2739.64692 
##          116          117          118          119          120          121 
##   4364.67658   3654.67265   -915.20859  -4570.22638  -3664.56823   2318.15035 
##          122          123          124          125          126          127 
##  -1699.41685   1344.91022   8882.50143    998.46165    275.83232  -2391.57496 
##          128          129          130          131          132          133 
##   2732.30457   7159.54811   1209.74301  -8311.47254   1789.95469   4197.39113 
##          134          135          136          137          138          139 
##  -3048.75646  -1364.58836   -825.81958  -3867.15953   1138.31769   -516.55661 
##          140          141          142          143          144          145 
##  -2938.54596   1654.52287  -1910.88669  -7882.12487   1879.64199  -3588.88657 
##          146          147          148          149          150          151 
##   1956.66872   -353.32617    936.13979   -419.65815   1294.83242   1156.88465 
##          152          153          154          155          156          157 
##   3348.54278  -4819.15286  -1207.57102  -3281.22706   5870.43536   9758.89301 
##          158          159          160          161          162          163 
##  -3545.44888  -4919.58691   3418.97164     93.72727   2618.62186  -5931.36316 
##          164          165          166          167          168          169 
##  -6843.89381   3982.13218  17307.73226   3806.08257   -185.07411  -2258.98061 
##          170          171          172          173          174          175 
##   -969.33876   3698.78963    -76.45815  -7940.04278   2873.14888   4380.39248 
##          176          177          178          179          180          181 
##    740.78269   8865.75888  -9018.05186  -3395.92024 -10721.89868 -11366.95628 
##          182          183          184          185          186          187 
##    970.47343   9085.65918  -1474.26304   5875.05589   6596.68186  13289.81644 
##          188          189          190          191          192          193 
##   8731.70584  -3682.07382   2740.90405  10644.22508  -1259.01957  -2131.75916 
##          194          195          196          197          198          199 
## -10041.11739  -6295.82492   1206.22193  -5237.49186  -9869.26388   5187.62564 
##          200          201          202          203          204          205 
##  -3160.53079  -1831.52926   -927.88273   6378.93216   9872.43872    705.51232 
##          206          207          208          209          210          211 
##   3044.23972   3243.24845   5954.56217  13062.34852  -5311.38326 -11044.78152 
##          212          213          214          215          216          217 
##  -5599.64347 -10603.89502  -5230.98546   1325.50944 -13161.05113  16084.51929 
##          218          219          220          221          222          223 
##   7770.63935   1602.76540  26775.85043  12964.81114   7886.30338  14606.04570 
##          224          225          226          227          228          229 
##  -3219.07618  -1189.05989   4235.16348    811.49268   3146.52697   9393.80319 
##          230          231          232          233          234          235 
##   6296.46932  -1415.20548  -1422.11098   9758.01394 -11087.14705  -7070.71435 
##          236          237          238          239          240          241 
##  -8452.57525 -10138.13238   2910.80103   1252.17648  -8362.26920  -9155.90178 
##          242          243          244          245          246          247 
##   8831.07076  -7867.86377   2295.83703 -10430.79779  -4303.43782   1153.68443 
##          248          249          250          251          252          253 
##    791.13703 -12483.98760   3333.02828   1844.28068   4052.00218   2054.44994 
##          254          255          256          257          258          259 
##  -1199.97765  11090.80639  20988.84888   3567.94269  -3907.58195   4367.93079 
##          260          261          262          263          264          265 
##  -1415.65287   3956.77753  -4611.27328 -10749.28022  -4748.01937   -601.05285 
##          266          267          268          269          270          271 
##  -5263.72854   8643.74537  -4281.75657   4130.67342  -2103.80417   4404.62101 
##          272          273          274          275          276          277 
##    743.06460   7340.20583  -1286.32008  12113.36081  -4354.81513   1860.82613 
##          278          279          280          281          282          283 
##   -235.60612   7962.15035  -4863.05703  -2630.16331 -11210.29406  -2769.84887 
##          284          285          286          287          288          289 
##  18533.16294   7914.98129   2944.62078   -414.23921   1080.95070   6558.56436 
##          290          291          292          293          294          295 
##   7099.59251 -18500.00290 -11139.74131  -8260.40405   9445.13665   3004.62084 
##          296          297          298          299          300          301 
##  -1198.13882  27371.47361  10379.21519   5295.94010   9919.26936   3320.57506 
##          302          303          304          305          306          307 
##   -596.64139   8259.89239 -23883.88573  -3475.76653   -164.98186  -6958.59682 
##          308          309          310          311          312          313 
##  -4045.37388   2824.12144  -9245.65092  -3383.25399  -8351.94536   1329.32237 
##          314          315          316          317          318          319 
##  -3330.10023   1861.08856  -4214.62796  27286.20977   -547.17252   3434.71610 
##          320          321          322          323          324          325 
##  10993.55446   5859.86445  32681.52059   5761.89485 -20310.45664   2085.61745 
##          326          327          328          329          330          331 
##   1387.56442  -6209.92295  -1588.31916 -33159.02705    619.56066  -2504.20554 
##          332          333          334          335          336          337 
##   -280.78926  -3317.13805   3932.90049   -507.97601  -7006.17669  -3231.19421 
##          338          339          340          341          342          343 
##  -2313.53210  -7796.68686   3674.65851  -1466.25287  -1822.89323  -1074.52088 
##          344          345          346          347          348          349 
##    108.50298    437.65374  -1638.66799  -9470.98245 -13329.05840   2066.32773 
##          350          351          352          353          354          355 
##  -4494.33205  -3843.34977  -6168.71309   1531.80565   1227.54690   2644.68505 
##          356          357          358          359          360          361 
##  -3818.70056   -594.75157    613.67502   6976.07909    332.66245     21.98104 
##          362          363          364          365          366          367 
##   2643.34394  -2660.19860   -820.83668  -8693.93976  -4669.00587  -6288.27657 
##          368          369          370          371          372          373 
##  -5070.92663  -7398.97274   4821.31028    271.16621   7048.79278  -7608.16364 
##          374          375          376          377          378          379 
##  -2311.71367  -3442.39433  -2538.59023 -12533.32917   1712.65879 -10764.04291 
##          380          381          382          383          384          385 
##   5479.26251   9223.82786   3142.10023  -2341.33839   1636.92259   6798.52862 
##          386          387          388          389          390          391 
##  11539.13818  -5561.92447  -5217.32061    -86.97611   8627.72256   1968.50584 
##          392          393          394          395          396          397 
##  11377.29651  -9620.41849   2888.68924    843.96684    685.77592   -538.70522 
##          398          399          400          401          402          403 
##   -468.17042 -14408.36261   8444.13742  -1144.81117  -1345.07477   6999.72155 
##          404          405          406          407          408          409 
##  -7833.60483  -1287.10281  -2525.13733  -5827.85870  -2914.64056  -3980.97479 
##          410          411          412          413          414          415 
##  -8837.48681   5983.98164   1590.42218  -7386.88225  -7769.50695  14086.34005 
##          416          417          418          419          420          421 
##   3860.21327   4576.66916  -7909.25713  -4711.01127  -2610.37232   2797.63663 
##          422          423          424          425          426          427 
## -13987.54542  -2906.70170  -9212.77971   2833.36322   6868.53514   6567.58038 
##          428          429          430          431          432          433 
##  -3914.32807  -4088.56472  -4725.35061  -1828.98297  -5751.14883  -6707.46472 
##          434          435          436          437          438          439 
##  -6075.63613  -1550.76971   -983.86075  -5087.18576   2446.17619   4763.84438 
##          440          441          442          443          444          445 
##  -5057.73654  -2199.94239   1530.95250  -3846.15241   2800.24319  -6565.11043 
##          446          447          448          449          450          451 
## -12156.12590  -4666.36084   9478.39115  -2056.82452   4725.84213  -5828.37510 
##          452          453          454          455          456          457 
##  -1135.79490    375.42433   3038.06564 -12211.01788   3300.08045  -6709.10974 
##          458          459          460          461          462          463 
##   6457.58293   3042.36951   2580.05773  -3741.52832   2155.01335     83.32959 
##          464          465          466          467          468          469 
##   1885.10094   -408.43261   3455.72745  -2498.33230   5912.14947  -6772.20901 
##          470          471          472          473          474          475 
##  -2877.65769  -2146.32983  -4620.08936   3000.20970   7849.66970  -5871.29538 
##          476          477          478          479          480          481 
##   1560.22810  -6080.79438  -2810.79469   2026.76926 -12878.29544  -9836.78950 
##          482          483          484          485          486          487 
##  -1363.90605   -115.44125  -1062.18575  -1422.01325  -9652.03782  10947.47576 
##          488          489          490          491          492          493 
##   6247.25811   7515.54751  -5257.03214   5477.63914   9462.83401   6317.61156 
##          494          495          496          497          498          499 
## -13165.01752 -10433.11194  -3426.46266  -1113.82198   -526.00997  -7615.76690 
##          500          501          502          503          504          505 
##    553.39164   4262.93858   5552.64497    776.62150    205.15808  -7113.59151 
##          506          507          508          509          510          511 
##    615.20697  -4985.22727   1848.64402  -1244.34063  -8109.65715   -633.62592 
##          512          513          514          515          516          517 
##  -2690.44687   -613.40669   1321.38320  -9471.92206  -7833.60886  24158.31984 
##          518          519          520          521          522          523 
##  10002.71740   6157.72610  -5014.87754   3030.88356  17266.08315  11890.12049 
##          524          525          526          527          528          529 
## -23648.86432  -4882.67903  -3621.15593   4643.28476   -229.00439 -10981.15056 
##          530          531          532          533          534          535 
##   4388.81273  13972.45096  -4752.78679   4526.02610   5749.26727  -1547.22511 
##          536          537          538          539          540          541 
##  -4336.89154  -6933.46039  -2041.71119   8366.63069    274.02196  -7997.63651 
##          542          543          544          545          546          547 
##   1865.82446   -519.74724    445.02738 -10942.27636 -11090.99859   1909.71521 
##          548          549          550          551          552          553 
##   6930.86038  -1284.96590    867.59279  -7667.72156   8542.00632    999.52287 
##          554          555          556          557          558          559 
## -11837.39578   9137.96650   8754.73226    293.99217   5037.19862  -3347.03023 
##          560          561          562          563          564          565 
##  14279.40506  21816.64961  -5941.60351  -9287.40254   7020.30573    526.49360 
##          566          567          568          569          570          571 
##   3729.73733  -7087.84676 -17125.10054   6646.25401   6512.68423   2058.52366 
##          572          573          574          575          576          577 
##   3271.17316   1970.43929  -1955.55041  14890.40549  -9320.14509  -6053.74794 
##          578          579          580          581          582          583 
##   8822.18436   3067.18071  -6317.82248   7648.44476  -3579.46292  -2611.53789 
##          584          585          586          587          588          589 
##  15829.93689 -14198.15179   8541.39168    278.76814  -6015.09932   -635.85914 
##          590          591          592          593          594          595 
##    363.56939 -10536.07919   1797.28602  -7105.04058   3040.66682   8896.46633 
##          596          597          598          599          600          601 
##  -7357.00154   5909.36136   2868.61541   7019.59995  -2954.33239   6334.98019 
##          602          603          604          605          606          607 
##  -8051.53430   2393.43933   1433.04248   3311.74902   1698.65227    608.32390 
##          608          609          610          611          612          613 
##  -5605.41340   8205.34898   -960.97778  -2375.95039  -3291.55853  -8107.49263 
##          614          615          616          617          618          619 
##  11990.36820   5119.38489  -9080.87144  11742.46751   6302.12905  -5266.60182 
##          620          621          622          623          624          625 
##  26581.61290 -12311.32708  -6459.61655   3377.83497  -3911.46294 -10404.72882 
##          626          627          628          629          630          631 
##  11358.32602 -21431.21973  -2467.12688   8623.63009  11212.61794  -1336.15484 
##          632          633          634          635          636          637 
##  33474.52115  -6016.49027   6155.24149   5864.96863  -1782.40894  -4930.01804 
##          638          639          640          641          642          643 
##  -1621.85016 -12157.20750  -2128.89131  -1792.89428  -2440.12670  -2796.47900 
##          644          645          646          647          648          649 
##   1856.58166   4509.47590  17105.72905  18893.08462   1413.05100   5276.95272 
##          650          651          652          653          654          655 
##  11109.33947  20725.01346   1501.50605 -27376.17899  -1052.30637  -2032.46211 
##          656          657          658          659          660          661 
##   2090.20397  -2952.37479 -10426.74733   1732.22747   4329.82941   -841.64200 
##          662          663          664          665          666          667 
##  13188.01107   1676.43786   2128.15378 -11372.03668   1538.88382   1367.58751 
##          668          669          670          671          672          673 
##  -4969.96867  -7276.99645   2118.35254  -3615.32914   2737.78197  -3264.49204 
##          674          675          676          677          678          679 
##  -9252.90515  -8323.50533  -3072.26016     77.05182   2790.14394    723.57690 
##          680          681          682          683          684 
##  -3783.93816  -1794.29049  -1303.77448  -8221.58398   4588.36191 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17334.15 20134.52 24318.55 24041.16 26355.83 23732.34 24436.88 19747.39 
##       10       11       12       13       14       15       16       17 
## 19488.28 16874.73 17639.41 14422.59 14472.91 15126.64 16795.32 15142.96 
##       18       19       20       21       22       23       24       25 
## 16160.62 15544.45 22510.44 21609.51 21097.92 22956.85 22293.92 22935.74 
##       26       27       28       29       30       31       32       33 
## 24751.34 18779.57 20477.34 28185.79 28242.50 27920.79 25589.18 26968.56 
##       34       35       36       37       38       39       40       41 
## 30748.40 31091.10 32477.10 30028.64 34060.21 37215.54 34320.05 31183.38 
##       42       43       44       45       46       47       48       49 
## 30050.10 20784.54 28179.63 30576.05 31647.34 38373.00 37875.90 42461.42 
##       50       51       52       53       54       55       56       57 
## 46628.03 39446.01 34103.78 29208.33 22465.49 28652.07 25288.93 21647.41 
##       58       59       60       61       62       63       64       65 
## 25983.28 27222.12 27514.25 27919.29 23858.12 40176.58 41990.21 37314.35 
##       66       67       68       69       70       71       72       73 
## 41452.11 46286.93 56779.67 54826.10 40311.96 37858.83 40827.11 35220.98 
##       74       75       76       77       78       79       80       81 
## 30748.01 21604.33 24753.16 20745.87 22797.63 17777.11 19759.39 18998.64 
##       82       83       84       85       86       87       88       89 
## 18042.35 16143.04 17399.80 20948.52 25293.59 26267.18 26291.73 26898.33 
##       90       91       92       93       94       95       96       97 
## 30955.57 29805.35 30788.27 28884.54 28097.52 28459.61 28859.50 22542.57 
##       98       99      100      101      102      103      104      105 
## 25508.02 18653.12 17528.06 15601.01 15895.84 16562.08 20961.16 20059.74 
##      106      107      108      109      110      111      112      113 
## 23513.10 23306.52 24955.07 27784.52 25323.79 21825.76 22096.65 24699.40 
##      114      115      116      117      118      119      120      121 
## 35385.21 33607.78 35415.04 38364.04 40287.78 38014.23 32920.43 29321.99 
##      122      123      124      125      126      127      128      129 
## 31370.56 29678.80 30840.93 38315.68 37964.02 37041.00 33956.12 35708.02 
##      130      131      132      133      134      135      136      137 
## 41017.11 40466.62 31813.05 33057.04 36194.33 32664.02 31077.82 30177.87 
##      138      139      140      141      142      143      144      145 
## 26791.54 28182.70 27956.12 25680.48 27671.60 26318.98 20026.36 23007.03 
##      146      147      148      149      150      151      152      153 
## 20869.47 23797.61 24328.72 25892.94 26072.02 27698.97 28978.31 31960.58 
##      154      155      156      157      158      159      160      161 
## 27505.29 26780.37 24375.85 30172.96 41565.88 39923.59 37331.89 42269.56 
##      162      163      164      165      166      167      168      169 
## 43654.95 47014.65 42555.18 37939.58 43275.55 59309.49 61485.22 59925.41 
##      170      171      172      173      174      175      176      177 
## 56803.34 55228.92 57887.03 56927.19 49346.14 52123.18 55804.22 55839.81 
##      178      179      180      181      182      183      184      185 
## 62851.34 53509.92 50314.33 41274.24 32952.81 36403.34 46340.55 45805.52 
##      186      187      188      189      190      191      192      193 
## 51660.32 57310.75 67916.29 73112.22 66910.67 67100.92 74054.88 69802.47 
##      194      195      196      197      198      199      200      201 
## 65398.97 54819.82 48948.21 50349.06 46016.26 38313.95 44632.96 42889.53 
##      202      203      204      205      206      207      208      209 
## 42533.45 43003.92 49686.13 58429.06 58064.76 59761.18 61389.72 65118.51 
##      210      211      212      213      214      215      216      217 
## 74429.24 66642.35 55025.79 49723.32 40867.84 37875.63 40938.05 31122.48 
##      218      219      220      221      222      223      224      225 
## 47816.65 55016.95 55904.01 78294.76 85666.41 87636.67 95103.08 86202.92 
##      226      227      228      229      230      231      232      233 
## 80300.12 79888.94 76594.04 75769.34 80428.39 81770.21 76297.25 71588.99 
##      234      235      236      237      238      239      240      241 
## 77149.58 64017.14 56184.72 48267.85 40017.48 44140.39 46257.70 39816.19 
##      242      243      244      245      246      247      248      249 
## 33599.79 43713.01 38054.59 41925.51 34316.72 33043.89 36639.01 39416.42 
##      250      251      252      253      254      255      256      257 
## 30396.83 36237.15 39976.00 45085.26 47758.83 47259.77 57391.15 74600.34 
##      258      259      260      261      262      263      264      265 
## 74418.44 67839.21 69296.65 65579.65 67001.99 60862.42 50313.59 46406.34 
##      266      267      268      269      270      271      272      273 
## 46612.30 42783.11 51442.33 47776.76 51855.23 50002.81 54003.22 54294.37 
##      274      275      276      277      278      279      280      281 
## 60212.75 57885.92 67399.67 61424.46 61631.03 60007.28 65655.63 59489.31 
##      282      283      284      285      286      287      288      289 
## 56109.72 45833.99 44257.12 61205.73 66644.81 67047.52 64507.62 63610.01 
##      290      291      292      293      294      295      296      297 
## 67545.12 71391.00 52700.31 42965.26 37074.86 47226.38 50414.85 49543.38 
##      298      299      300      301      302      303      304      305 
## 73341.50 79189.06 79845.73 84382.28 82610.50 77722.54 81132.31 56444.20 
##      306      307      308      309      310      311      312      313 
## 52766.84 52451.88 46344.23 43599.59 47143.65 39818.40 38561.52 33212.53 
##      314      315      316      317      318      319      320      321 
## 36934.81 36129.63 39898.06 37915.65 63277.74 61154.43 62751.30 70617.85 
##      322      323      324      325      326      327      328      329 
## 72965.91 98028.39 96432.74 72660.53 71478.15 69862.49 61946.60 59116.17 
##      330      331      332      333      334      335      336      337 
## 29558.87 33185.78 33618.07 35899.85 35251.53 40923.69 41981.61 37307.34 
##      338      339      340      341      342      343      344      345 
## 36534.67 36659.26 32055.20 37955.54 38608.04 38862.24 39723.64 41480.20 
##      346      347      348      349      350      351      352      353 
## 43272.24 43027.98 36088.63 26811.53 32068.33 30948.06 30544.86 28200.48 
##      354      355      356      357      358      359      360      361 
## 32802.45 36495.03 40885.27 39104.04 40343.61 42446.92 49720.62 50262.16 
##      362      363      364      365      366      367      368      369 
## 50460.51 52883.20 50407.98 49861.65 42627.72 39870.56 36110.36 33925.54 
##      370      371      372      373      374      375      376      377 
## 30048.12 37216.26 39465.64 47221.59 41292.29 40748.54 39309.88 38850.33 
##      378      379      380      381      382      383      384      385 
## 29868.06 34390.61 27556.45 35640.74 45804.04 49310.91 47612.65 49571.61 
##      386      387      388      389      390      391      392      393 
## 55689.58 65019.21 58342.03 52901.12 52634.28 59892.64 60407.42 68933.70 
##      394      395      396      397      398      399      400      401 
## 58218.31 59759.46 59326.80 58819.13 57330.88 56112.79 43088.86 51533.53 
##      402      403      404      405      406      407      408      409 
## 50550.36 49533.56 55829.75 48494.67 47817.14 46171.29 41919.50 40769.40 
##      410      411      412      413      414      415      416      417 
## 38865.06 33056.16 40799.72 43678.03 38437.79 33606.66 48234.22 52015.90 
##      418      419      420      421      422      423      424      425 
## 55880.69 48473.44 44857.09 43554.79 47082.40 35691.56 35425.21 29778.21 
##      426      427      428      429      430      431      432      433 
## 35276.32 43467.28 50246.33 47064.85 44181.64 41157.27 41047.29 37582.89 
##      434      435      436      437      438      439      440      441 
## 33784.64 31064.06 32614.29 34433.33 32470.68 37257.01 43360.74 40166.37 
##      442      443      444      445      446      447      448      449 
## 39877.19 42834.30 40755.04 44679.11 40003.98 31183.36 30039.89 41210.54 
##      450      451      452      453      454      455      456      457 
## 40897.30 46455.80 42163.51 42507.43 44101.36 47758.59 37798.92 42568.68 
##      458      459      460      461      462      463      464      465 
## 38066.99 45511.92 48974.23 51551.81 48334.99 50637.38 50835.61 52554.00 
##      466      467      468      469      470      471      472      473 
## 52059.84 54955.33 52327.42 57295.78 50666.23 48316.33 46925.66 43605.36 
##      474      475      476      477      478      479      480      481 
## 47299.90 54640.87 49159.20 50834.51 45708.79 44114.37 46900.87 36488.65 
##      482      483      484      485      486      487      488      489 
## 30155.76 31994.44 34646.90 36112.44 37062.47 30807.52 43132.31 49683.31 
##      490      491      492      493      494      495      496      497 
## 56401.60 51199.79 55953.59 63462.10 67211.02 53692.68 44425.03 42482.39 
##      498      499      500      501      502      503      504      505 
## 42800.30 43578.48 38155.61 40515.20 45729.78 51318.24 52016.27 52125.02 
##      506      507      508      509      510      511      512      513 
## 45930.22 47248.23 43568.78 46279.05 45950.23 39769.05 40881.59 40070.26 
##      514      515      516      517      518      519      520      521 
## 41157.76 43754.49 36712.04 32068.82 55566.71 63593.56 67186.59 60674.26 
##      522      523      524      525      526      527      528      529 
## 61991.77 75354.59 82216.86 57577.96 52532.16 49280.72 53587.86 53102.29 
##      530      531      532      533      534      535      536      537 
## 43446.90 48356.83 60809.64 55420.40 58762.30 62684.65 59785.61 54897.89 
##      538      539      540      541      542      543      544      545 
## 48467.43 47145.37 54952.26 54706.78 47388.89 49576.03 49405.54 50087.99 
##      546      547      548      549      550      551      552      553 
## 40890.43 32860.14 37130.71 45114.11 44914.41 46592.29 40700.42 49565.48 
##      554      555      556      557      558      559      560      561 
## 50701.82 40648.75 50033.12 57766.86 57142.23 60680.89 56517.59 68085.06 
##      562      563      564      565      566      567      568      569 
## 84499.75 74753.40 63504.69 67851.36 66006.55 67173.70 58882.10 43134.03 
##      570      571      572      573      574      575      576      577 
## 50027.60 55835.76 56999.11 59040.56 59676.98 56850.59 68896.15 58444.03 
##      578      579      580      581      582      583      584      585 
## 52270.10 59746.82 61226.11 54433.56 60597.18 56245.97 53339.06 66686.29 
##      586      587      588      589      590      591      592      593 
## 52354.18 59577.80 58685.10 52510.43 51827.00 52098.51 42966.86 45717.75 
##      594      595      596      597      598      599      600      601 
## 40432.48 44608.53 53227.86 46668.64 52431.38 54770.11 60346.05 56567.31 
##      602      603      604      605      606      607      608      609 
## 61301.96 53009.13 54858.24 55621.82 57892.06 58456.68 58004.98 52278.08 
##      610      611      612      613      614      615      616      617 
## 59223.69 57315.66 54460.56 51220.78 44299.35 55620.47 59444.01 50528.39 
##      618      619      620      621      622      623      624      625 
## 60759.44 64875.60 58472.39 80334.61 65701.90 58157.31 60127.32 55557.01 
##      626      627      628      629      630      631      632      633 
## 46051.25 56582.65 37458.56 37321.08 46732.10 57042.44 55119.19 83375.92 
##      634      635      636      637      638      639      640      641 
## 73723.47 75888.03 77498.41 72311.45 65150.42 61840.06 49943.89 48339.04 
##      642      643      644      645      646      647      648      649 
## 47248.84 45756.05 44167.28 46800.10 51341.56 66066.20 80253.23 77423.90 
##      650      651      652      653      654      655      656      657 
## 78312.80 84087.70 97311.21 92156.04 62915.16 60408.89 57413.37 58381.80 
##      658      659      660      661      662      663      664      665 
## 54881.32 45451.77 47796.88 52043.64 51249.13 62620.70 62500.42 62785.18 
##      666      667      668      669      670      671      672      673 
## 51430.54 52767.70 53769.40 49184.85 43263.65 46248.61 43886.93 47316.35 
##      674      675      676      677      678      679      680      681 
## 45105.76 38061.22 32807.12 32804.66 35508.43 40162.57 42385.80 40423.15 
##      682      683      684 
## 40446.35 40887.73 35323.21 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8252
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original    bias    std. error
## t1*    7.858502  0.525039    3.516451
## t2* 1903.449860 23.800742  227.245129
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.326722       8.002477   14.70108
## 2    lag_depvar 1572.034211    1913.905168 2322.88616

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Mar 11 00:38:42 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 11 00:38:49 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 11 00:38:56 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 11 00:39:03 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 11 00:39:10 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 11 00:39:16 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 11 00:39:23 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 11 00:39:30 2024
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## =-=-=-=-= Iteration 16000 Mon Mar 11 00:39:37 2024
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## =-=-=-=-= Iteration 18000 Mon Mar 11 00:39:44 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 0.0000 5.195333 5.410333 5.849167 6.5345098
Comida 229.6570 366.009167 310.278417 317.896583 338.4755882
Comunicaciones 0.0000 0.000000 0.000000 0.000000 0.0000000
Electricidad 83.7810 38.104750 47.072333 29.523000 35.1219216
Enceres 55.1865 18.259750 20.086417 14.801167 23.9398039
Farmacia 0.0000 4.733250 1.831667 13.996083 8.1406471
Gas/Bencina 35.4985 35.219333 44.325000 13.583667 27.3653529
Diosi 0.0000 55.804250 31.180667 52.687833 42.0119608
donaciones/regalos 0.0000 0.000000 0.000000 14.340167 5.3866471
Electrodomésticos/ Mantención casa 30.0000 0.000000 3.944000 56.595000 17.4405490
VTR 21.9900 12.829167 25.156667 19.086917 18.7885882
Netflix 8.3485 4.555500 7.151583 7.028750 6.6763529
Otros 0.0000 0.000000 3.151083 0.000000 0.7414314
Total 464.4615 540.710500 499.588167 545.388333 530.6233529
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2260, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-03-09 00:04:58 sería de: 37.693 pesos// Percentil 95% más alto proyectado: 40.841,28

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36984.46 36981.15
Lo.80 37035.93 37046.95
Point.Forecast 37693.41 39585.60
Hi.80 39470.68 44491.94
Hi.95 40444.78 47089.19


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2841  1016.1292
## s.e.  0.1275    29.2255
## 
## sigma^2 = 27810:  log likelihood = -397.69
## AIC=801.38   AICc=801.81   BIC=807.72
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2395   518.7119  15.9282
## s.e.  0.1274   255.9436   8.1350
## 
## sigma^2 = 26670:  log likelihood = -395.88
## AIC=799.76   AICc=800.48   BIC=808.21
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 833.4420 675.2312 745.5439
Lo.80 947.5539 793.2280 832.9201
Point.Forecast 1163.1165 1016.1291 1026.8549
Hi.80 1378.6791 1239.0303 1265.8909
Hi.95 1492.7911 1357.0271 1414.1699


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))